We review different technologies and architectures for neuromorphic photonic accelerators,
spanning from bulk optics to photonic-integrated-circuits (PICs), and assess compute …

Photonic computation has garnered huge attention due to its great potential to accelerate
artificial neural network tasks at much higher clock rate to digital electronic alternatives …

Recent advances in neuromorphic computing have established a computational framework
that removes the processor-memory bottleneck evident in traditional von Neumann …

This paper proposes a large-scale, energy-efficient, high-throughput, and compact
tensorized optical neural network (TONN) exploiting the tensor-train decomposition …

Progress in computing architectures is approaching a paradigm shift: traditional computing
based on digital complementary metal-oxide semiconductor technology is nearing physical …

A nanophotonic neural network (N‐PNN) architecture is proposed with compact
nanophotonic scattering units and a hybrid structure of silicon and nonvolatile arrayed …

We discuss the design and demonstration of various III–V/Si asymmetric Mach–Zehnder
interferometer (AMZI) and ring-assisted AMZI (de-) interleavers operating at O-band …

Spiking neural networks (SNN) provide a new computational paradigm capable of highly
parallelized, real-time processing. Photonic devices are ideal for the design of high …

Photonic convolution processing units are the core components of photonic neural networks
(PNNs). Traditionally, for an N× N photonic convolution processing unit, it typically requires …

The human brain has immense learning capabilities at extreme energy efficiencies and
scale that no artificial system has been able to match. For decades, reverse engineering the …